iAR: 'iAR' Class

In iAR: Irregularly Observed Autoregressive Models

'iAR' Class

Description

Represents a univariate irregular autoregressive (iAR) time series model. This class extends the "unidata" class and includes additional properties for modeling, forecasting, and interpolation.

Description: Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>)

Usage

iAR(
  times = integer(0),
  series = integer(0),
  series_esd = integer(0),
  family = "norm",
  fitted_values = integer(0),
  loglik = integer(0),
  kalmanlik = integer(0),
  coef = integer(0),
  df = integer(0),
  sigma = integer(0),
  mean = integer(0),
  variance = integer(0),
  tAhead = 1,
  forecast = integer(0),
  interpolated_values = integer(0),
  interpolated_times = integer(0),
  interpolated_series = integer(0),
  zero_mean = TRUE,
  standardized = TRUE,
  hessian = FALSE,
  summary = list()
)

Arguments

times	A numeric vector representing the time points.
series	A numeric vector representing the values of the time series.
series_esd	A numeric vector representing the error standard deviations of the time series.
family	A character string indicating the distribution family of the model (default: "norm").
fitted_values	A numeric vector containing the fitted values from the model.
loglik	A numeric value representing the log-likelihood of the model.
kalmanlik	A numeric value representing the Kalman likelihood of the model.
coef	A numeric vector containing the estimated coefficients of the model.
df	A numeric value representing the degrees of freedom ('t' distribution).
sigma	A numeric value representing the scale parameter ('t' distribution).
mean	A numeric value representing the estimated mean of the model ('gamma' parameter).
variance	A numeric value representing the estimated variance of the model ('gamma' parameter).
tAhead	A numeric value specifying the forecast horizon (default: 1).
forecast	A numeric vector containing the forecasted values.
interpolated_values	A numeric vector containing the interpolated values.
interpolated_times	A numeric vector containing the times of the interpolated data points.
interpolated_series	A numeric vector containing the interpolated series.
zero_mean	A logical value indicating if the model assumes a zero-mean process (default: TRUE).
standardized	A logical value indicating if the model assumes a standardized process (default: TRUE).
hessian	A logical value indicating whether the Hessian matrix is computed during estimation (default: FALSE).
summary	A list containing the summary of the model fit, including diagnostics and statistical results.

Details

The 'iAR' class is designed to handle irregularly observed time series data using an autoregressive approach. It extends the "unidata" class to include additional modeling and diagnostic capabilities. Key functionalities include forecasting, interpolation, and model fitting.

The class also supports advanced modeling features, such as: - Different distribution families for the data (e.g., Gaussian, 't'-distribution). - Optional computation of the Hessian matrix for parameter estimation. - Standardized or zero-mean process assumptions.

BIAR functions

The foo functions ...

CIAR functions

The foo functions ...

IAR functions

heloo

"_PACKAGE"

References

\insertRef

Eyheramendy_2018iAR

Examples

# Create an `iAR` object
o=iAR::utilities()
o<-gentime(o, n=200, distribution = "expmixture", lambda1 = 130, lambda2 = 6.5,p1 = 0.15, p2 = 0.85)
times=o@times
my_iAR <- iAR(family = "norm", times = times, coef = 0.9,hessian=TRUE)

my_iAR@family
my_iAR@coef

iAR documentation built on April 4, 2025, 2:21 a.m.